code: def __init__(self, in_channels, dim_reduced, num_classes):
      super().__init__(
          OrderedDict(
              [
                  (
                      "conv5_mask",
                      nn.ConvTranspose2d(
                          in_channels, dim_reduced, 2, 2, 0
                      ),
                  ),
                  ("relu", nn.ReLU(inplace=True)),
                  (
                      "mask_fcn_logits",
                      nn.Conv2d(dim_reduced, num_classes, 1, 1, 0),
                  ),
              ]
          )
      )
  
      for name, param in self.named_parameters():
          if "weight" in name:
              nn.init.kaiming_normal_(
                  param, mode="fan_out", nonlinearity="relu"
              )
  
imports: from collections import OrderedDict
  from typing import Any, Callable, Optional
  from torch import nn
  from torchvision.ops import MultiScaleRoIAlign
  from ...ops import misc
  from ...transforms._presets import ObjectDetection
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _COCO_CATEGORIES
  from .._utils import _ovewrite_value_param, handle_legacy_interface
  from ..resnet import resnet50, ResNet50_Weights
  from ._utils import overwrite_eps
  from .backbone_utils import _resnet_fpn_extractor, _validate_trainable_layers
  from .faster_rcnn import _default_anchorgen, FasterRCNN, FastRCNNConvFCHead, RPNHead
label_desc: Use image node_id 1 to create a new MaskRCNNPredictor object from inherited base classes: nn with example: obj = MaskRCNNPredictor(in_channels, dim_reduced, num_classes)